The present invention relates to prediction of events, more particularly to predictive and informative methods that involve leading indicator analysis.
Leading Indicator (LI) analysis is a methodology of predicting future events based on past performance. It involves a measurement or combination of measurements that, when properly analyzed, can provide a prediction of a potential future occurrence. Leading indicators are used regularly in business and finance to predict performance (e.g., of a company or an industry) for purposes such as making more informed investment decisions.
Leading indicator analysis is now beginning to make its way into aviation safety realms with various aims, such as prediction (and hopefully, avoidance) of mishaps, maintenance or improvement of efficient operating practices, evaluation of training methods, and identification of underperforming (or outperforming) pilots or aircraft. Leading indicators can be used for evaluating many aspects of aircrew and aircraft performance. Leading indicator analysis can be applied to any observable or experiential system of or related to an air vehicle—that is, any system that has or can have data associated with it, including engines, structural components, pilot, and aircrew.
Current leading indicator approaches typically rely on comparing data to a desired outcome. For instance, let us assume that, according to a “rule,” a commercial aircraft is not supposed to exceed an air speed of 700 knots during normal operations. According to a conventional leading indicator analytical method, flight data is analyzed to determine whether or not any pilots have exceeded this air speed limit. This kind of approach—in which comparison of data is made with respect to a “rule”—is inherently constrained in that it requires that limits be known, documented, and identifiable in the available data.
Conventional leading indicator analysis methods are “rule comparative” in nature. A rule comparative leading indicator (RCLI) compares an individual event to one or more “rules” that have been established. By setting one or more limits, a “rule” serves to guide, prescribe or prohibit conduct, action or behavior. For instance, in the aircraft safety realm, an RCLI can compare an individual flight to regulations, restrictions, standard operating procedures, and/or system operating limitations, to determine compliance of the individual flight in one or more of these respects. An RCLI is obtained through analyzing data from a single flight, followed by comparing these data to a set of predetermined rules.
RCLI analyses can indicate aircrew compliance or non-compliance with rules and guidelines, or can indicate systems operation inside or outside prescribed limits. Violation of policy may be interpreted as a precursor to a mishap, i.e., as a leading indicator to a potential mishap. Similarly, an indication of system operation beyond prescribed limits may be interpreted as a leading indicator to a system failure or malfunction, or to an accelerated usage rate. If a leading indicator is addressed, this might prevent occurrence in the future of the undesirable circumstance. RCLI analyses can be conducted to identify not only policy violations but also policy near-violations as well as unsafe (albeit technically or marginally compliant) practices.
Leading Indicator analysis is typically multidimensional, concerning multiple aspects and yielding multiple pieces of information per aspect. Often the resultant data do not lend themselves to display via standard techniques of plotting or data visualization. The kind of information generated through leading indicator analysis may be understood by an expert or analyst, but a decision-maker may be hard pressed to make much sense of it.
Currently unknown is an automated system for presenting leading indicator analytical information directly to decision-makers in a manner easily intelligible to them. Conventional presentational methods usually constitute manual assemblages of information by analysts and experts, compiled into reports or slideshows, and then presented to the decision-makers. An automated, straightforward, and easily comprehensible presentation method is needed in order to utilize leading indicator analytical information to the fullest.
Many modern aircraft are equipped with at least one advanced aircraft data recorder (ADR), designed to track maintenance-related information. A maintenance-dedicated ADR may encompass system fault information, mechanical parameters, flight control parameters, air data, or other data deemed relevant to system diagnostics or prognostics. Automatically and continually, the data are collected and processed using onboard diagnostic systems and/or specially configured ground stations post-flight. The recorded data are designed to be used for specific purposes, typically for fault-based maintenance and/or condition-based maintenance and/or component usage tracking, depending on the maintenance scheme implemented for the aircraft or subsystem.
Present day aircraft data recorders enable flight data analysis that is continual, rather than only if a mishap occurs. Aircraft data recorders may contain various types of data including systems data, flight parameters data, control inputs and outputs, and flight events (e.g. stall, weight-on-wheels, gear movement, etc.). Because these aircraft data recorders are principally used for maintenance, they are downloaded on a regular basis. In contrast, “crash survivable” data recorders are downloaded only after a mishap. Although ADR usage has been primarily relegated to supporting maintenance processes, the potential exists for harvesting much more information from these recorded data, using modern methods of data analysis and advanced statistical analysis. Technical specifications for these types of data—e.g., recording rate, accuracy, precision, resolution, and the conditions under which the data are recorded—may also be useful.
Through the years there have been various initiatives to improve flight safety and maximize operational efficiency. New regulations have built upon preceding regulations to improve the safety of aircraft, both mechanically and operationally. With reference to FIG. 1, technical and programmatic changes effected by United States Naval aviation have resulted in significant safety improvements and associated mishap reductions. On the one hand, the recent U.S. Naval aviation mishap rate is a fraction of what it once was; on the other hand, the recent U.S. Naval aviation mishap rate has stabilized in the last decade or so, no longer showing a decreasing trend. Safety issues similarly remain for commercial airlines to address. Future breakthroughs in flight safety may have much to do with greater abilities to derive valuable safety information preemptively rather than after the fact.